Vladimir (Vlejd) is an AI Resident at Google AI in NYC. His journey into machine learning began when he received his bachelors and masters degree in computer science from Comenius University in Bratislava. During his studies, he worked at two machine learning startups, and interned twice at Google Zurich working on security incident visualizations and YouTube video deduplication.
Vladimir is currently interested in enhancing AutoML algorithms by leveraging unsupervised techniques and reinforcement learning.
As part of his residency, Vladimir currently works on AdaNet, an instance of the AutoML family that seeks to adaptively learn both the architecture and the weights of a deep neural network, and that benefits from strong learning guarantees (Cortes et al., ICML, 2017). The algorithm leverages the recent deep boosting theory (Cortes et al., ICML 2014), and is closely related to ensemble methods.
His previously work was on applied machine learning systems for customer screening, real estate appraisal and patent recommendations, and on various other software engineering projects.
Vladimir chose the AI residency as a preparation/alternative for PhD. He was not sure what part of machine learning he would like to pursue and residency provides him opportunities to try multiple projects. To quote Vladimir: “My assumptions were correct and Yes, it is really as good as you think!”